Full-Field Optical Coherence Tomography (FFOCT) is an effective technique for tissue anatomy imaging, allowing cancer detection through the observation of disorders in the tissue microarchitecture. Moreover, with the temporal analysis of the FFOCT signal variations, a functional dimension is added, allowing the distinction of cell types through their intracellular activity. This complementary imaging mode, called Dynamic Cell Imaging (DCI), has shown the ability to identify normal cells, cancer cells and immune cells in different types of tissue such as breast, liver or lung.
On samples ranging from cell cultures to entire tissue resections, DCI signals are recorded and analyzed in order to characterize the involved endogenous biomarker at the subcellular scale. Longitudinal studies of these samples over a few hours are performed under different environment perturbations intended to modify the cell metabolism.
The potential of bimodal FFOCT is evaluated through a clinical study organized at the department of breast surgery of Peking University People’s Hospital in Beijing. More than 200 breast samples and lymph nodes are included. A part of the images will be directly compared to histology to identify reading criteria and build a reference atlas. The other part will be read on a blind study to measure the ability of cancer detection with respect to histology.
To take full advantage of the FFOCT and the DCI information richness for faster cancer assessment during surgeries, a computer-aided diagnostic system based on Machine Learning, and more specifically Deep Learning, is investigated.